Yield formation of soybean under the influence of inoculation and top-dressing
Bibliographic record
Abstract
Purpose. To establish reaction of soybean varieties to seed inoculation by bacterial preparation and to foliar nutrition of plants by chelate microfertilizers. Methods. Field studies were carried out in 2017, 2018 in the zone of Western Polissia on sod podzol soils of stationary crop rotation of the ALLC Vasiuty (Kovel district, Volyn region) with the inoculant Legume Fix based on bacteria Bradyrhizobium japonicum 532c and the preparations of foliar nutrition Wuxal Оilseed and Quantum-Oil on the soybean varieties Canadian Kassidy and French Mentor. The planting practice was typical for the zone. Phenological observations and yield counts in different in weather conditions years of research were carried out according to conventional methods for soybeans, yielding capacity was determined by the method of full threshing plot with combine. The part of the factor influence on yield was determined by ANOVA at 5 % significance level. Results. In 2017, in dry unfavorable weather conditions of the year the average seed yield for the soybean variety Kassidy was 2.17 t/ha, for the Mentor it was 2.25 t/ha, whereas in favorable 2018 it was respectively 3.13 t/ha and 3.20 t/ha. Yield increase due to seed inoculation was 0.09–0.21 t/ha in the dry year (2017) and 0.15–0.35 t/ha in sufficient rainfed environments (2018). Yield increase due to foliar fertilization by chelate microfertilizers in the phase of budding was higher (0.15–0.36 t/ha) than the seed inoculation, and the efficiency of this practice did not depend on the year weather conditions. The maximum yield was formed in favorable 2018 by the variety Mentor when applying seed inoculation and crop treatment with Wuxal Oilseed (3.45 t/ha), and by the Kassidy variety when using seed inoculation and crop treatment with Quantum-Oil (3.37 t/ha). Conclusions. Seed treatment by the inoculant Legume Fix and foliar nutrition with chelate microfertilizers have significant effect on soybean yielding capacity in different year weather conditions. The interaction of these factors occurs to less extent than their individual effects. The efficiency of the foliar nutrition on yielding capacity in the unfavorable 2017 was 64 %, whereas in the favorable 2018 it was 50 %. The efficiency of seed treatment with inoculant was more in sufficient rainfed environments in 2018 (40 %) than in the arid 2017 (24 %). The part of influence of varietal characteristics on yielding capacity under favorable conditions decreased from 9 to 4 %.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".